Paper Contents
Abstract
ABSTRACTThe fast-growing online recruitment marketplace has given job seekers the option of finding opportunities worldwide. Nevertheless, it has also served as the base to an even-increasing number of job scams that prey on would-be candidates. This study shows a machine learning approach to fake job advertisements detection. The application of natural language processing (NLP) and various classifiers using the fake job posting dataset available on Kaggle is used to separate the legitimate and fake job posting. The implementation will involve the incorporation of a web-based application that was created using Flask, which allows users to submit job descriptions and get real-time results regarding the classification. The tests conducted on the proposed system indicate that it is highly accurate hence potentially protecting job seekers against scams.INTRODUCTIONOnline job sites like LinkedIn, Indeed and Naukri.com have made a change in the job sector. However, unfortunately, they become the target of fraudsters placing fraudulent adverts of vacancies in order to obtain personal data, money, or identity of job seekers. The conventional methods of detection are manual based, and hence time-consuming and unreliable. In order to overcome this challenge, machine learning has automated approaches that can be used to analyze job postings and detect linguistic and structural patterns, and labeling them as genuine or fake. The proposed research is on the development of a Smart Job Scam Detection System along with building a web-based interface application.
Copyright
Copyright © 2025 MANOJ KUMAR. This is an open access article distributed under the Creative Commons Attribution License.
